{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "[Installation](installing.rst)\n",
    "[Tutorials](tutorials.rst)\n",
    "[Examples](examples.rst)\n",
    "[Gallery](gallery.rst)\n",
    "[API](api.rst)\n",
    "[Datasets](datasets.rst)\n",
    "[FAQ](faq.rst)"
   ]
  },
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    "<style>\n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Vaex?\n",
    "\n",
    "Vaex is a python library for lazy **Out-of-Core DataFrames** (similar to Pandas), to visualize and explore big tabular datasets. It can calculate *statistics* such as mean, sum, count, standard deviation etc, on an *N-dimensional grid* up to **a billion** ($10^9$) objects/rows **per second**. Visualization is done using **histograms**, **density plots** and **3d volume rendering**, allowing interactive exploration of big data. Vaex uses memory mapping, a zero memory copy policy, and lazy computations for best performance (no memory wasted)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why vaex\n",
    " \n",
    " * **Performance:** works with huge tabular data, processes $\\gt 10^9$ rows/second\n",
    " * **Lazy / Virtual columns:** compute on the fly, without wasting ram\n",
    " * **Memory efficient** no memory copies when doing filtering/selections/subsets.\n",
    " * **Visualization:** directly supported, a one-liner is often enough.\n",
    " * **User friendly API:** you will only need to deal with the DataFrame object, and tab completion + docstring will help you out: `ds.mean<tab>`, feels very similar to Pandas.\n",
    " * **Lean:** separated into multiple packages\n",
    "    * `vaex-core`: DataFrame and core algorithms, takes numpy arrays as input columns.\n",
    "    * `vaex-hdf5`: Provides memory mapped numpy arrays to a DataFrame.\n",
    "    * `vaex-arrow`: [Arrow](https://arrow.apache.org/) support for cross language data sharing.\n",
    "    * `vaex-viz`: Visualization based on matplotlib.\n",
    "    * `vaex-jupyter`: Interactive visualization based on Jupyter widgets / ipywidgets, bqplot, ipyvolume and ipyleaflet.\n",
    "    * `vaex-astro`: Astronomy related transformations and FITS file support.\n",
    "    * `vaex-server`: Provides a server to access a DataFrame remotely.\n",
    "    * `vaex-distributed`: (Proof of concept) combined multiple servers / cluster into a single DataFrame for distributed computations.\n",
    "    * `vaex-qt`: Program written using Qt GUI.\n",
    "    * `vaex`: Meta package that installs all of the above.\n",
    "    * `vaex-ml`: [Machine learning](ml.ipynb)\n",
    "\n",
    " * **Jupyter integration**: vaex-jupyter will give you interactive visualization and selection in the Jupyter notebook and Jupyter lab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "Using conda: \n",
    "\n",
    " * `conda install -c conda-forge vaex`\n",
    "\n",
    "Using pip:\n",
    "\n",
    " * `pip install --upgrade vaex`\n",
    "  \n",
    "Or read the [detailed instructions](installing.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started\n",
    "\n",
    "We assume that you have installed vaex, and are running a [Jupyter notebook server](https://jupyter.readthedocs.io/en/latest/running.html). We start by importing vaex and asking it to give us an example dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vaex\n",
    "df = vaex.example()  # open the example dataset provided with vaex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead, you can [download some larger datasets](datasets.rst), or [read in your csv file](api.rst#vaex.from_csv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                                  </th><th>x           </th><th>y           </th><th>z           </th><th>vx         </th><th>vy         </th><th>vz         </th><th>E              </th><th>L                 </th><th>Lz                 </th><th>FeH                </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i>      </td><td>-0.777470767</td><td>2.10626292  </td><td>1.93743467  </td><td>53.276722  </td><td>288.386047 </td><td>-95.2649078</td><td>-121238.171875 </td><td>831.0799560546875 </td><td>-336.426513671875  </td><td>-2.309227609164518 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i>      </td><td>3.77427316  </td><td>2.23387194  </td><td>3.76209331  </td><td>252.810791 </td><td>-69.9498444</td><td>-56.3121033</td><td>-100819.9140625</td><td>1435.1839599609375</td><td>-828.7567749023438 </td><td>-1.788735491591229 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i>      </td><td>1.3757627   </td><td>-6.3283844  </td><td>2.63250017  </td><td>96.276474  </td><td>226.440201 </td><td>-34.7527161</td><td>-100559.9609375</td><td>1039.2989501953125</td><td>920.802490234375   </td><td>-0.7618109022478798</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i>      </td><td>-7.06737804 </td><td>1.31737781  </td><td>-6.10543537 </td><td>204.968842 </td><td>-205.679016</td><td>-58.9777031</td><td>-70174.8515625 </td><td>2441.724853515625 </td><td>1183.5899658203125 </td><td>-1.5208778422936413</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i>      </td><td>0.243441463 </td><td>-0.822781682</td><td>-0.206593871</td><td>-311.742371</td><td>-238.41217 </td><td>186.824127 </td><td>-144138.75     </td><td>374.8164367675781 </td><td>-314.5353088378906 </td><td>-2.655341358427361 </td></tr>\n",
       "<tr><td>...                                </td><td>...         </td><td>...         </td><td>...         </td><td>...        </td><td>...        </td><td>...        </td><td>...            </td><td>...               </td><td>...                </td><td>...                </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>329,995</i></td><td>3.76883793  </td><td>4.66251659  </td><td>-4.42904139 </td><td>107.432999 </td><td>-2.13771296</td><td>17.5130272 </td><td>-119687.3203125</td><td>746.8833618164062 </td><td>-508.96484375      </td><td>-1.6499842518381402</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>329,996</i></td><td>9.17409325  </td><td>-8.87091351 </td><td>-8.61707687 </td><td>32.0       </td><td>108.089264 </td><td>179.060638 </td><td>-68933.8046875 </td><td>2395.633056640625 </td><td>1275.490234375     </td><td>-1.4336036247720836</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>329,997</i></td><td>-1.14041007 </td><td>-8.4957695  </td><td>2.25749826  </td><td>8.46711349 </td><td>-38.2765236</td><td>-127.541473</td><td>-112580.359375 </td><td>1182.436279296875 </td><td>115.58557891845703 </td><td>-1.9306227597361942</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>329,998</i></td><td>-14.2985935 </td><td>-5.51750422 </td><td>-8.65472317 </td><td>110.221558 </td><td>-31.3925591</td><td>86.2726822 </td><td>-74862.90625   </td><td>1324.5926513671875</td><td>1057.017333984375  </td><td>-1.225019818838568 </td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>329,999</i></td><td>10.5450506  </td><td>-8.86106777 </td><td>-4.65835428 </td><td>-2.10541415</td><td>-27.6108856</td><td>3.80799961 </td><td>-95361.765625  </td><td>351.0955505371094 </td><td>-309.81439208984375</td><td>-2.5689636894079477</td></tr>\n",
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       "#        x             y             z             vx           vy           vz           E                L                   Lz                   FeH\n",
       "0        -0.777470767  2.10626292    1.93743467    53.276722    288.386047   -95.2649078  -121238.171875   831.0799560546875   -336.426513671875    -2.309227609164518\n",
       "1        3.77427316    2.23387194    3.76209331    252.810791   -69.9498444  -56.3121033  -100819.9140625  1435.1839599609375  -828.7567749023438   -1.788735491591229\n",
       "2        1.3757627     -6.3283844    2.63250017    96.276474    226.440201   -34.7527161  -100559.9609375  1039.2989501953125  920.802490234375     -0.7618109022478798\n",
       "3        -7.06737804   1.31737781    -6.10543537   204.968842   -205.679016  -58.9777031  -70174.8515625   2441.724853515625   1183.5899658203125   -1.5208778422936413\n",
       "4        0.243441463   -0.822781682  -0.206593871  -311.742371  -238.41217   186.824127   -144138.75       374.8164367675781   -314.5353088378906   -2.655341358427361\n",
       "...      ...           ...           ...           ...          ...          ...          ...              ...                 ...                  ...\n",
       "329,995  3.76883793    4.66251659    -4.42904139   107.432999   -2.13771296  17.5130272   -119687.3203125  746.8833618164062   -508.96484375        -1.6499842518381402\n",
       "329,996  9.17409325    -8.87091351   -8.61707687   32.0         108.089264   179.060638   -68933.8046875   2395.633056640625   1275.490234375       -1.4336036247720836\n",
       "329,997  -1.14041007   -8.4957695    2.25749826    8.46711349   -38.2765236  -127.541473  -112580.359375   1182.436279296875   115.58557891845703   -1.9306227597361942\n",
       "329,998  -14.2985935   -5.51750422   -8.65472317   110.221558   -31.3925591  86.2726822   -74862.90625     1324.5926513671875  1057.017333984375    -1.225019818838568\n",
       "329,999  10.5450506    -8.86106777   -4.65835428   -2.10541415  -27.6108856  3.80799961   -95361.765625    351.0955505371094   -309.81439208984375  -2.5689636894079477"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df  # will pretty print the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using [square brackets[]](api.rst#vaex.dataframe.DataFrame.__getitem__), we can easily filter or get different views on the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>#                            </th><th style=\"text-align: right;\">         x</th><th style=\"text-align: right;\">       y</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td><i style='opacity: 0.6'>0</i></td><td style=\"text-align: right;\"> -0.777471</td><td style=\"text-align: right;\"> 2.10626</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>1</i></td><td style=\"text-align: right;\"> -7.06738 </td><td style=\"text-align: right;\"> 1.31738</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>2</i></td><td style=\"text-align: right;\"> -5.17174 </td><td style=\"text-align: right;\"> 7.82915</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>3</i></td><td style=\"text-align: right;\">-15.9539  </td><td style=\"text-align: right;\"> 5.77126</td></tr>\n",
       "<tr><td><i style='opacity: 0.6'>4</i></td><td style=\"text-align: right;\">-12.3995  </td><td style=\"text-align: right;\">13.9182 </td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "  #           x         y\n",
       "  0   -0.777471   2.10626\n",
       "  1   -7.06738    1.31738\n",
       "  2   -5.17174    7.82915\n",
       "  3  -15.9539     5.77126\n",
       "  4  -12.3995    13.9182"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_negative = df[df.x < 0]  # easily filter your DataFrame, without making a copy\n",
    "df_negative[:5][['x', 'y']]  # take the first five rows, and only the 'x' and 'y' column (no memory copy!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When dealing with huge datasets, say a billion rows ($10^9$), computations with the data can waste memory, up to 8 GB for a new column. Instead, vaex uses lazy computation, storing only a representation of the computation, and computations are done on the fly when needed. You can just use many of the numpy functions, as if it was a normal array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<vaex.expression.Expression(expressions='(x + z)')> instance at 0x118f71550 values=[1.159963903, 7.53636647, 4.00826287, -13.17281341, 0.036847591999999985 ... (total 330000 values) ... -0.66020346, 0.5570163800000003, 1.1170881900000003, -22.95331667, 5.8866963199999995] "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# creates an expression (nothing is computed)\n",
    "some_expression = df.x + df.z\n",
    "some_expression  # for convenience, we print out some values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These expressions can be added to a DataFrame, creating what we call a *virtual column*. These virtual columns are similar to normal columns, except they do not waste memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.06713149126400597, -0.0501732470530304)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['r'] = some_expression  # add a (virtual) column that will be computed on the fly\n",
    "df.mean(df.x), df.mean(df.r)  # calculate statistics on normal and virtual columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the core features of vaex is its ability to calculate statistics on a regular (N-dimensional) grid. The dimensions of the grid are specified by the binby argument (analogous to SQL's grouby), and the shape and limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.67777315, -8.99466731, -8.17042477, -7.57122871, -6.98273954,\n",
       "       -6.28362848, -5.70005784, -5.14022306, -4.52820368, -3.96953423,\n",
       "       -3.3362477 , -2.7801045 , -2.20162243, -1.57910621, -0.92856689,\n",
       "       -0.35964342,  0.30367721,  0.85684123,  1.53564551,  2.1274488 ,\n",
       "        2.69235585,  3.37746363,  4.04648274,  4.59580105,  5.20540601,\n",
       "        5.73475069,  6.28384101,  6.67880226,  7.46059303,  8.13480148,\n",
       "        8.90738265,  9.6117928 ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(df.r, binby=df.x, shape=32, limits=[-10, 10]) # create statistics on a regular grid (1d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22., 33., 37., ..., 58., 38., 45.],\n",
       "       [37., 36., 47., ..., 52., 36., 53.],\n",
       "       [34., 42., 47., ..., 59., 44., 56.],\n",
       "       ...,\n",
       "       [73., 73., 84., ..., 41., 40., 37.],\n",
       "       [53., 58., 63., ..., 34., 35., 28.],\n",
       "       [51., 32., 46., ..., 47., 33., 36.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(df.r, binby=[df.x, df.y], shape=32, limits=[-10, 10]) # or 2d\n",
    "df.count(df.r, binby=[df.x, df.y], shape=32, limits=[-10, 10]) # or 2d counts/histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These one and two dimensional grids can be visualized using any plotting library, such as matplotlib, but the setup can be tedious. For convenience we can use [plot1d](api.rst#vaex.dataframe.DataFrame.plot1d), [plot](api.rst#vaex.dataframe.DataFrame.plot), or see the [list of plotting commands](api.rst#visualization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(df.x, df.y, show=True);  # make a plot quickly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continue\n",
    "[Continue the tutorial here](tutorial.ipynb) or check the [examples](examples.rst)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
